Notice

Dependencies

AFNI

Python 2.7

numpy

scipy

Installation

Install Python and other dependencies. If you have AFNI installed and on your path, you should already have an up-to-date version of ME-ICA on your path. Running meica.py without any options will check for dependencies and let you know if they are met. If you don't have numpy/scipy (or appropriate versions) installed, I would strongly recommend using the Enthought Canopy Python Distribution. Click here for more installation help.

-e 15,30,45 are the echo times in milliseconds
-d rest_e1.nii.gz,rest_e2... are the 4-D time series datasets (comma separated list of dataset of each TE) from a multi-echo fMRI acqusition
-a ... is a "raw" mprage with a skull
-b 15 means drop first 15 seconds of data for equilibration
--MNI warp anatomical to MNI space using a built-in high-resolution MNI template.
--prefix sub1_rest prefix for final functional output datasets, i.e. sub1_rest_....nii.gz

Again, see meica.py -h for handling other situations such as: anatomical with no skull, no anatomical at all, applying FWHM smoothing, non-linear warp to standard space, etc.

Output

sub1_rest_medn.nii.gz : 'Denoised' BOLD time series after: basic preprocessing, T2* weighted averaging of echoes (i.e. 'optimal combination'), ICA denoising. Use this dataset for task analysis and resting state time series correlation analysis. See here for information on degrees of freedom in denoised data.

For a step-by-step guide on how to assess ME-ICA results in more detail, click here

Some Notes

Make sure your datasets have slice timing information in the header. If not sure, specify a --tpattern option to meica.py. Check AFNI documentation of 3dTshift to see slice timing codes.

For more info on T2* weighted anatomical-functional coregistration click here

FWHM smoothing is not recommended. tSNR boost is provided by optimal combination of echoes. For better overlap of 'blobs' across subjects, use non-linear standard space normalization instead with meica.py ... --qwarp